Scientific Publication Speed of Korean Medical Journals during the COVID-19 Era.

OBJECTIVES: This study compared the scientific publication speeds of Korean medical journals before and during the coronavirus disease 2019 (COVID-19) era. METHODS: We analyzed 2,064 papers from 43 international Korean medical journals, selecting 12 papers annually from 2019 to 2022. We assessed publication speed indicators, including the time from submission to revision and from submission to publication. Additionally, we examined variations in publication speed based on journal and paper characteristics, including whether the studies were related to COVID-19. RESULTS: Among the 43 journals analyzed, 39.5% disclosed the peer review duration from submission to the first decision, and 11.6% reported their acceptance rates. The average time from submission to acceptance was 127.0 days in 2019, 126.1 days in 2020, 124.6 days in 2021, and 126.4 days in 2022. For COVID-19-related studies, the average time from submission to revision was 61.4 days, compared to 105.1 days for non-COVID-19 studies; from submission to acceptance, it was 87.4 days for COVID-19-related studies and 127.1 days for non-COVID-19 studies. All indicators for COVID-19-related studies showed shorter durations than those for non-COVID-19 studies, and the proportion of studies accepted within 30 or 60 days was significantly higher for COVID-19-related studies. CONCLUSIONS: This study investigated the publication speed of Korean international medical journals before and during the COVID-19 pandemic. The pandemic influenced journals' review and publication processes, potentially impacting the quality of academic papers. These findings provide insights into publication speeds during the COVID-19 era, suggesting that journals should focus on maintaining the integrity of their publication and review processes.

Blockchain-Based Caching Architecture for DApp Data Security and Delivery.

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model's effectiveness in providing secure and high-performance DApp solutions.

Exploring the Relationship Between Avatar Ego Types and Communication: A Comparative Study of South Korea and the United States Based on the Expectation Confirmation Model.

This research explores the significance of avatar communication in the virtual world, where individuals can create new identities and establish relationships beyond real-world limitations. Avatar users engage in virtual interactions to fulfill their desires, enjoy entertainment, and experience surrogate satisfaction. This study integrates the Expectation Confirmation Model (ECM) and Impression Management Theory (IMT) to investigate the impact of various avatar ego types on communication satisfaction and continued intention to use. Two surveys (n = 600) were administered using South Korean and American samples. The results suggest a significant relationship between expectancy confirmation and perceived usefulness. Specifically, high perceived usefulness leads to increased communication satisfaction. Also, when pre-experience expectancy confirmation is low, it positively affects communication satisfaction. In addition, the study highlights differences between Western and Eastern cultural contexts in avatar ego type's expression. This study contributes to the understanding of virtual interactions, offering theoretical insights through the integration of ECM and IMT. Theoretical and practical implications are discussed.

Long-term humoral and cellular immunity against vaccine strains and Omicron subvariants (BQ.1.1, BN.1, XBB.1, and EG.5) after bivalent COVID-19 vaccination.

Background: The assessment of long-term humoral and cellular immunity post-vaccination is crucial for establishing an optimal vaccination strategy. Methods: This prospective cohort study evaluated adults (≥18 years) who received a BA.4/5 bivalent vaccine. We measured the anti-receptor binding domain immunoglobulin G antibody and neutralizing antibodies (NAb) against wild-type and Omicron subvariants (BA.5, BQ.1.1, BN.1, XBB.1 and EG.5) up to 9 months post-vaccination. T-cell immune responses were measured before and 4 weeks after vaccination. Results: A total of 108 (28 SARS-CoV-2-naïve and 80 previously infected) participants were enrolled. Anti-receptor binding domain immunoglobulin G (U/mL) levels were higher at 9 months post-vaccination than baseline in SAR-CoV-2-naïve individuals (8,339 vs. 1,834, p<0.001). NAb titers against BQ.1.1, BN.1, and XBB.1 were significantly higher at 9 months post-vaccination than baseline in both groups, whereas NAb against EG.5 was negligible at all time points. The T-cell immune response (median spot forming unit/106 cells) was highly cross-reactive at both baseline (wild-type/BA.5/XBB.1.5, 38.3/52.5/45.0 in SARS-CoV-2-naïve individuals; 51.6/54.9/54.9 in SARS-CoV-2-infected individuals) and 4 weeks post-vaccination, with insignificant boosting post-vaccination. Conclusion: Remarkable cross-reactive neutralization was observed against BQ.1.1, BN.1, and XBB.1 up to 9 months after BA.4/5 bivalent vaccination, but not against EG.5. The T-cell immune response was highly cross-reactive.

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Novel Enhanced Mammalian Cell Transient Expression Vector via Promoter Combination.

During the emergence of infectious diseases, evaluating the efficacy of newly developed vaccines requires antigen proteins. Available methods enhance antigen protein productivity; however, structural modifications may occur. Therefore, we aimed to construct a novel transient overexpression vector capable of rapidly producing large quantities of antigenic proteins in mammalian cell lines. This involved expanding beyond the exclusive use of the human cytomegalovirus (CMV) promoter, and was achieved by incorporating a transcriptional enhancer (CMV enhancer), a translational enhancer (woodchuck hepatitis virus post-transcriptional regulatory element), and a promoter based on the CMV promoter. Twenty novel transient expression vectors were constructed, with the vector containing the human elongation factor 1-alpha (EF-1a) promoter showing the highest efficiency in expressing foreign proteins. This vector exhibited an approximately 27-fold higher expression of enhanced green fluorescent protein than the control vector containing only the CMV promoter. It also expressed the highest level of severe acute respiratory syndrome coronavirus 2 receptor-binding domain protein. These observations possibly result from the simultaneous enhancement of the transcriptional activity of the CMV promoter and the human EF-1a promoter by the CMV enhancer. Additionally, the synergistic effect between the CMV and human EF-1a promoters likely contributed to the further enhancement of protein expression.

Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma.

Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS.

Immunogenicity and safety of concomitant bivalent COVID-19 and quadrivalent influenza vaccination: implications of immune imprinting and interference.

OBJECTIVES: Concomitant COVID-19 and influenza vaccination would be an efficient strategy. Although the co-administration of monovalent COVID-19 and influenza vaccinations showed acceptable immunogenicity, it remains unknown whether the bivalent COVID-19 vaccine could intensify immune interference. We aimed to evaluate the immunogenicity and safety of concomitant BA.5-based bivalent COVID-19 and influenza vaccination. METHODS: An open-label, nonrandomized clinical trial was conducted for 154 age-matched and sex-matched healthy adults between October 2022 and December 2022. Participants received either a concomitant bivalent COVID-19 mRNA booster and quadrivalent influenza vaccination (group C) or separate vaccinations (group S) at least 4 weeks apart. Solicited and unsolicited adverse events were reported up to 6 months postvaccination. Immunogenicity was evaluated by anti-spike (S) IgG electrochemiluminescence immunoassay, focus reduction neutralization test, and hemagglutination inhibition assay. RESULTS: Group C did not meet the noninferiority criteria for the seroconversion rates of anti-S IgG and neutralizing antibodies against the wild-type SARS-CoV-2 strain compared with group S (44.2% vs. 46.8%, difference of -2.6% [95% CI, -18 to 13.4]; 44.2% vs. 57.1%, difference of -13.0% [95% CI to -28.9 to 2.9]). However, group C showed a stronger postvaccination neutralizing antibody response against Omicron BA.5 (72.7% vs. 64.9%). Postvaccination geometric mean titers for SARS-CoV-2 and influenza strains were similar between groups, except for influenza B/Victoria. Most adverse events were mild and comparable between the study groups. DISCUSSION: Concomitant administration of bivalent COVID-19 mRNA and quadrivalent influenza vaccines showed tolerable safety profiles and sufficient immunogenicity, particularly attenuating immune imprinting induced by previous ancestral vaccine strains.

Molecular data representation based on gene embeddings for cancer drug response prediction.

Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as gene expression, they employ a one-hot encoding-based approach, where a fixed gene set is selected for all samples and omics data values are assigned to specific positions in a vector. However, this approach restricts the utilization of embedding-vector-based methods, such as attention-based models, and limits the flexibility of gene selection. To address these issues, our study proposes gene embedding-based fully connected neural networks (GEN) that utilizes gene embedding vectors as input data for cancer drug response prediction. The GEN allows for the use of embedding-vector-based architectures and different gene sets for each sample, providing enhanced flexibility. To validate the efficacy of GEN, we conducted experiments on three cancer drug response datasets. Our results demonstrate that GEN outperforms other recently developed methods in cancer drug prediction tasks and offers improved gene representation capabilities. All source codes are available at https://github.com/DMCB-GIST/GEN/ .

A Federated Blockchain Architecture for File Storage with Improved Latency and Reliability in IoT DApp Services.

Blockchain technology can address data falsification, single point of failure (SPOF), and DDoS attacks on centralized services. By utilizing IoT devices as blockchain nodes, it is possible to solve the problem that it is difficult to ensure the integrity of data generated by using current IoT devices. However, as the amount of data generated by IoT devices increases, scalability issues are inevitable. As a result, large amounts of data are managed on external cloud storage or distributed file storage. However, this has the disadvantage of being outside the blockchain network. This makes it difficult to ensure reliability and causes high latency during data download and upload. To address these limitations, we propose a method for managing large amounts of data in the local storage node of a blockchain network with improved latency and reliability. Each blockchain network node stores data, which is synchronized and recovered based on reaching a consensus between smart contracts in a cluster network. The cluster network consists of a service leader node that serves as a gateway for services and a cluster node that stores service data in storage. The blockchain network stores synchronization and recovery metadata created in the cluster network. In addition, we showed that the performance of smart contract execution, network transmission, and metadata generation, which are elements of the proposed consensus process, is not significantly affected. In addition, we built a service leader node and a cluster node by implementing the proposed structure. We compared the performance (latency) of IoT devices when they utilized the proposed architecture and existing external distributed storage. Our results show improvements up to 4 and 10 times reduction in data upload (store) and download latency, respectively.

Neutralizing Activity against BQ.1.1, BN.1, and XBB.1 in Bivalent COVID-19 Vaccine Recipients: Comparison by the Types of Prior Infection and Vaccine Formulations.

Bivalent COVID-19 vaccines that contain BA.1 or BA.4/BA.5 have been introduced worldwide in response to pandemic waves of Omicron subvariants. This prospective cohort study was aimed to compare neutralizing antibodies (Nabs) against Omicron subvariants (BA.1, BA.5, BQ.1.1, BN.1, and XBB.1) before and 3-4 weeks after bivalent booster by the types of SARS-CoV-2 variants in prior infections and bivalent vaccine formulations. A total of 21 participants were included. Prior BA.1/BA.2-infected, and BA.5-infected participants showed significantly higher geometric mean titers of Nab compared to SARS-CoV-2-non-infected participants after bivalent booster (BA.1, 8156 vs. 4861 vs. 1636; BA.5, 6515 vs. 4861 vs. 915; BQ.1.1, 697 vs. 628 vs. 115; BN.1, 1402 vs. 1289 vs. 490; XBB.1, 434 vs. 355 vs. 144). When compared by bivalent vaccine formulations, Nab titers against studied subvariants after bivalent booster did not differ between BA.1 and BA.4/BA.5 bivalent vaccine (BA.1, 4886 vs. 5285; BA.5, 3320 vs. 4118; BQ.1.1, 311 vs. 572; BN.1, 1028 vs. 1095; XBB.1, 262 vs. 362). Both BA.1 and BA.4/BA.5 bivalent vaccines are immunogenic and provide enhanced neutralizing activities against Omicron subvariants. However, even after the bivalent booster, neutralizing activities against the later Omicron strains (BQ.1.1, BN.1, and XBB.1) would be insufficient to provide protection.

Influence of Isolation Temperature on Isolating Diverse Lactic Acid Bacteria from Kimchi and Cultural Characteristics of Psychrotrophs.

Kimchi is a traditional Korean fermented vegetable that is stored and fermented at low temperatures. However, kimchi lactic acid bacteria (LAB) are typically isolated under mesophilic conditions, which may be inappropriate for isolating the diverse LAB. Therefore, this study investigated the suitable conditions for isolating various LAB from kimchi. Here, LAB were isolated from four kimchi samples using MRS, PES, and LBS media and varying isolation temperatures (30, 20, 10, and 5°C). Then, MRS was selected as the suitable medium for LAB isolation. A comparison of culture-dependent and culture-independent approaches indicated that 5°C was not a suitable isolation temperature. Thus, the number and diversity of LAB were determined at 30, 20, and 10°C using 12 additional kimchi samples to elucidate the effect of isolation temperature. With the exception of two samples, most samples did not substantially differ in LAB number. However, Leuconostoc gelidum, Leuconostoc gasicomitatum, Leuconostoc inhae, Dellaglioa algida, Companilactobacillus kimchiensis, Leuconostoc miyukkimchii, Leuconostoc holzapfelii, and Leuconostoc carnosum were isolated only at 10 and 20°C. The growth curves of these isolates, except Leu. holzapfelii and Leu. carnosum, showed poor growth at 30°C. This confirmed their psychrotrophic characteristics. In Weissella koreensis, which was isolated at all isolation temperatures, there was a difference in the fatty acid composition of membranes between strains that could grow well at 30°C and those that could not. These findings can contribute to the isolation of more diverse psychrotrophic strains that were not well isolated under mesophilic temperatures.

Autophagic activation in porcine oocytes is independent of meiotic progression.

In this study, we built on our previous research that discovered that autophagy activated the metaphase I stage during porcine oocytes in vitro maturation. We investigated the relationship between autophagy and oocyte maturation. First, we confirmed whether autophagy was activated differently by different media (TCM199 and NCSU-23) during maturation. Then, we investigated whether oocyte maturation affected autophagic activation. In addition, we examined whether the inhibition of autophagy affected the nuclear maturation rate of porcine oocytes. As for the main experiment, we measured LC3-II levels using western blotting after inhibition of nuclear maturation via cAMP treatment in an in vitro culture to clarify whether nuclear maturation affected autophagy. After autophagy inhibition, we also counted matured oocytes by treating them with wortmannin or a E64d and pepstatin A mixture. Both groups, which had different treatment times of cAMP, showed the same levels of LC3-II, while the maturation rates were about four times higher after cAMP 22 h treatment than that of the 42 h treatment group. This indicated that neither cAMP nor nuclear status affected autophagy. Autophagy inhibition during in vitro oocyte maturation with wortmannin treatment reduced oocyte maturation rates by about half, while autophagy inhibition by the E64d and pepstatin A mixture treatment did not significantly affect the oocyte maturation. Therefore, wortmannin itself, or the autophagy induction step, but not the degradation step, is involved in the oocyte maturation of porcine oocytes. Overall, we propose that oocyte maturation does not stand upstream of autophagy activation, but autophagy may exist upstream of oocyte maturation.

IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security.

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

Superhydrophobic polypropylene sorbent derived from discarded face masks: A highly efficient adsorbent for oil spill sorbent.

Globally, an estimated 130 billion face masks are used and disposed of every month. Thus, recycling or upcycling discarded face masks has attracted significant attention due to economic benefits and environmental concerns. To reduce the amount of used face masks going to waste, this study features a superhydrophobic face mask prepared by simple chemical modification with environmentally preferable alkane solvents (n-hexane, n-heptane, and n-decane), that is effective as a sorbent for oil spill cleanup. All alkanes examined increased the surface roughness of the face masks and improved face mask hydrophobicity. The heptane treated face mask (at 90 °C for 1 h), can adsorbed Arabian light crude oil up to 21 times of their weight on the water surface. In addition, chloroform, toluene, gasoline, and diesel were adsorbed 18, 13, 8 and 16 times, respectively. More importantly, heptane has a high recycling efficiency as a treatment solvent and is reusable for at least 10 cycles of mask surface treatment. Consequently, this inexpensive and easily fabricated material is a promising development in waste face mask (WFM) upcycling.

HIDTI: integration of heterogeneous information to predict drug-target interactions.

Identification of drug-target interactions (DTIs) plays a crucial role in drug development. Traditional laboratory-based DTI discovery is generally costly and time-consuming. Therefore, computational approaches have been developed to predict interactions between drug candidates and disease-causing proteins. We designed a novel method, termed heterogeneous information integration for DTI prediction (HIDTI), based on the concept of predicting vectors for all of unknown/unavailable heterogeneous drug- and protein-related information. We applied a residual network in HIDTI to extract features of such heterogeneous information for predicting DTIs, and tested the model using drug-based ten-fold cross-validation to examine the prediction performance for unseen drugs. As a result, HIDTI outperformed existing models using heterogeneous information, and was demonstrating that our method predicted heterogeneous information on unseen data better than other models. In conclusion, our study suggests that HIDTI has the potential to advance the field of drug development by accurately predicting the targets of new drugs.

A cystic artery arising from the middle hepatic artery detected during laparoscopic cholecystectomy: a case report.

We herein report a case in which a cystic artery arising from the middle hepatic artery (MHA) was encountered during cholecystectomy. A 42-year-old man visited the outpatient department complaining of chronic epigastric pain and a laparoscopic cholecystectomy was decided upon. During the preoperative evaluation, an interesting finding was observed on computed tomography. The patient had a cystic artery arising from the MHA. An MHA derived from the hilum of the proper hepatic artery runs along the medial border of the gallbladder and enters segment IV. In the operative field, the MHA that entered segment IV was observed. Dissecting downward along the MHA, a cystic artery was observed around the cystic duct. The cystic artery and duct were isolated and each was separately ligated and cut. Rare anatomical variations of the cystic artery, as in this case, can be a pitfall inducing complications during cholecystectomy.

Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System.

Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).

COVID-19 Outbreak in a Military Unit in Korea.

OBJECTIVES: This study presents the response of a military unit to the COVID-19 outbreak in Gyeonggi Province. As soon as two soldiers were identified as index cases, the infectious disease investigators of the Gyeonggi Provincial Government, Korea Disease Control and Prevention Agency and the Armed Forces Epidemiologic Investigation Center, discussed the investigation and response plan for an imminent massive outbreak. METHODS: The joint immediate response team (IRT) conducted interviews with confirmed patients with COVID-19, reviewed medical records, performed contact tracing using global positioning system (GPS), and undertook a field investigation. For risk assessment, the joint IRT visited all eight sites of the military units and the army chaplain's church to evaluate the transmission risk of each site. The evaluation items included the size of the site, the use of air conditioning, whether windows were opened, and whether masks were worn. A pooled testing was used for a low-risk population to quickly detect the spread of COVID-19 in the military base. RESULTS: A day before the symptom onset of the index case, the lecturer and >50% of the attendees were infected with COVID-19 while attending a lecture that lasted 2 h and 30 min. Attendees were not wearing masks and were in a poorly ventilated room. CONCLUSION: Since the disease can be spread before symptom onset, contact tracing must be performed to investigate potential exposures prior to symptom onset and manage any exposed persons.

Prediction of Myoelectric Biomarkers in Post-Stroke Gait.

Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.